{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "changed-military",
   "metadata": {},
   "source": [
    "# ML 4 Timeseries: Mortality Prediction\n",
    "In this lab you will learn and implement a basic machine learning pipeline for time-series analysis mortality  prediction using ScikitLearn.\n",
    "\n",
    "Lab designed by: Anshul Thakur anshul.thakur@eng.ox.ac.uk<br>\n",
    "For more details on the lab, please see our [github repository](https://github.com/AnshThakur/CDT-TimeSeries)\n",
    "\n",
    "## Your tasks:\n",
    "1. Visualse the distribution of features & an example over time\n",
    "2. Generate statistical summary features\n",
    "    - $\\bar{\\mathbf{x}}$\n",
    "    - $\\bar{\\mathbf{x}}$ $\\pm$ $\\sigma$\n",
    "    - $\\bar{\\mathbf{x}}$ $\\pm$ $\\tilde{\\mathbf{x}}$\n",
    "3. Implement stratified k-fold cross validation\n",
    "4. Apply the necessay pre-processing steps \n",
    "    - normalisation\n",
    "    - outlier removal: mean imputation\n",
    "5. Train two off-the-shelf machine learning models for mortality prediction. \n",
    "    - Train a linear model, e.g. Logistic Regression\n",
    "    - Train a non-linear model, e.g. Random Forest\n",
    "6. Evaluate the model\n",
    "    - Compare between model perfromance\n",
    "    - Determine the important features for mortality prediction\n",
    "    - Compare model performance between various types of features\n",
    "7. Re-implement time-series specific features\n",
    "    - $\\mathbf{a}_{l}$, the autocorrelation coefficients at various time lags, $l$\n",
    "    - feel free to come up with your own, for example windowing the time-series\n",
    "    - train your models again and compare model performance between statistcial and time-series feature types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "boolean-lightweight",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import math\n",
    "import warnings\n",
    "import itertools\n",
    "#import numbers\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
    "\n",
    "import os\n",
    "import pickle\n",
    "from os.path import expanduser\n",
    "home = expanduser(\"~\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "established-waters",
   "metadata": {},
   "source": [
    "## Load the data:\n",
    "The data (``X_data.npy``) is is stored as $\\mathbf{X} \\in \\mathbb{R}^{N\\times T \\times P}$, where $N$ are the number of patients, $T$ are the number of time-steps and $P$ are the number of features. A corresponding column vector of labels (``y_data.npy``), $\\mathbf{y} \\in \\mathbb{R}^{N\\times 1}$, denote patient mortality at time $T+1$ A list of the clinical measurement names (i.e. the features) are stored in ``feature_names.txt``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "devoted-stuart",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape (N x T x P): (9622, 48, 59)\n",
      "y shape: (9622,)\n",
      "number of features 59\n",
      "features names: ['Diastolic blood pressure' 'GCS (total)' 'GCS 10' 'GCS5' 'GCS_1' 'GCS_11'\n",
      " 'GCS_12' 'GCS_13' 'GCS_14' 'GCS_15' 'GCS_16' 'GCS_17' 'GCS_18' 'GCS_19'\n",
      " 'GCS_2' 'GCS_20' 'GCS_21' 'GCS_22' 'GCS_23' 'GCS_24' 'GCS_24' 'GCS_25'\n",
      " 'GCS_26' 'GCS_27' 'GCS_27' 'GCS_28' 'GCS_29' 'GCS_3' 'GCS_30' 'GCS_31'\n",
      " 'GCS_32' 'GCS_33' 'GCS_34' 'GCS_35' 'GCS_35' 'GCS_36' 'GCS_37' 'GCS_38'\n",
      " 'GCS_38' 'GCS_38' 'GCS_39' 'GCS_4' 'GCS_40' 'GCS_6' 'GCS_7' 'GCS_8'\n",
      " 'GCS_9' 'Heart Rate' 'Height' 'ICU_Cardiac Surgery Recovery Unit'\n",
      " 'ICU_Coronary Care Unit' 'Mean blood pressure' 'Oxygen saturation'\n",
      " 'Respiratory rate' 'Sex' 'Systolic blood pressure' 'Temperature' 'Weight'\n",
      " 'pH']\n"
     ]
    }
   ],
   "source": [
    "'''load the data'''\n",
    "X=np.load('../../data/X_data.npy')  \n",
    "y=np.load('../../data/y_data.npy')\n",
    "feature_names=pd.read_csv('../../data/feature_names.txt').to_numpy().reshape(-1)\n",
    "\n",
    "print('X shape (N x T x P):', X.shape)\n",
    "print('y shape:', y.shape)\n",
    "print('number of features', len(feature_names))\n",
    "print('features names:',feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "respiratory-general",
   "metadata": {},
   "source": [
    "We can see that the features are various physiological measurements, such as heart rate, blood pressure and respiratory rates. Other measurements include the patient's demographics, such as sex, or physical attributes such as weght and height. A clinican-rated scoring system is also recorded, known as the Glasgow Coma Scale (GCS). These consist questions from three domains, (1) Eye response, (2) Verbal response (3) Motor response. These features and sub-domains have been coded as binary response or categorical variables. For more information see\n",
    "https://www.glasgowcomascale.org/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "designing-cooper",
   "metadata": {},
   "source": [
    "## 1. Task: Visualse the distribution of features & an example over time\n",
    "- <b>Hint</b>: summerise the features over time first using the median or mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "empirical-copper",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkl5g1l75pwt2bENfkjoy8dBPsjHJg0l2J7ly0ueXpJ5NNPSTLAN+E3gLcBbwL5OcNckxSFLPJn2lfx6wu6oerqrvADcBmyY8Bknq1vIJn2818NjY+h7gdVM7JdkCbGmr307y4PM838p8kKee575L1Uqw5g70VnNv9ZIPHnbNL5+ucdKhn2na6qCGquuA6w77ZMmOqlp/uMdZSqy5D73V3Fu9sHA1T3p6Zw9w+tj6GuDxCY9Bkro16dD/K2BdkjOSHAVsBrZNeAyS1K2JTu9U1YEk7wL+DFgGfKyqdi7gKQ97imgJsuY+9FZzb/XCAtWcqoOm1CVJRyj/IleSOmLoS1JHjojQn+vRDhm5um3/SpLXLsY458uAev9Vq/MrSb6Q5DWLMc75NPTxHUn+aZLnkrxtkuNbCENqTvLGJPcm2ZnkLyY9xvk24N/2S5L8cZIvt5rfsRjjnC9JPpbkyST3z7B9/rOrqpb0i9EXwv8TeAVwFPBl4KwpfS4EbmP0dwIbgC8u9rgXuN4fAVa05bcs5XqH1jzW77PAJ4G3Lfa4J/BzPgl4AHhZWz9lscc9gZrfB3ywLa8CvgYctdhjP4ya3wC8Frh/hu3znl1HwpX+kEc7bAJurJG7gJOSnDbpgc6TOeutqi9U1dfb6l2M/h5iKRv6+I6fBz4OPDnJwS2QITX/FPCJqnoUoKqWet1Dai7ghCQBjmcU+gcmO8z5U1WfY1TDTOY9u46E0J/u0Q6rn0efpeJQa7mM0ZXCUjZnzUlWAz8JfGSC41pIQ37OrwJWJLkjyT1JLpnY6BbGkJo/DPwAoz/qvA94T1V9dzLDWxTznl2TfgzDQhjyaIdBj39YIgbXkuRNjEL/xxZ0RAtvSM0fAt5bVc+NLgKXvCE1LwfOBc4HjgXuTHJXVf3NQg9ugQyp+QLgXuDHgVcCtyf5y6r61gKPbbHMe3YdCaE/5NEOR9LjHwbVkuQHgY8Cb6mqpyc0toUypOb1wE0t8FcCFyY5UFV/NJERzr+h/66fqqpngGeSfA54DbBUQ39Ize8APlCjCe/dSR4BXg3cPZkhTty8Z9eRML0z5NEO24BL2jfhG4BvVtXeSQ90nsxZb5KXAZ8AfnoJX/WNm7PmqjqjqtZW1VrgFuDfLOHAh2H/rm8FXp9keZLjGD2xdteExzmfhtT8KKPfbEhyKnAm8PBERzlZ855dS/5Kv2Z4tEOSn2vbP8Lobo4Lgd3A3zO6WliSBtb7y8BLgWvale+BWsJPKBxY8xFlSM1VtSvJp4CvAN8FPlpV0976txQM/Dn/J+D6JPcxmvp4b1Ut2UcuJ/l94I3AyiR7gPcDL4aFyy4fwyBJHTkSpnckSQMZ+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakj/w9kDhoxSMgYNQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4wPZQmgK6KGl5umtnXWkfMzOrQZUr/eE8DPRKWg+cBtYCRMRRSb3AMeAysDEirqR9NgA7gOnAvvQyM7OajCr0I+IJ4Im0/jywYph2PUBPg3ofsGS0nTQzs7Hh38g1M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwyUuXB6LdJOiTp65KOSvrNVJ8lab+kk2k5s7TPZkn9kk5IWlmqL5V0JG3bkh6baGZmNalypX8JeGdE3A3cA6yStBzYBByIiHbgQHqPpEUUz9JdDKwCtkqako61DeimeG5ue9puZmY1qfJg9IiIF9PbV6RXAKuBnam+E1iT1lcDuyPiUkScAvqBZZLmATMi4mBEBLCrtI+ZmdWg0py+pCmSngYuAPsj4ilgbkScA0jLOal5K3CmtPtAqrWm9aH1RufrltQnqW9wcHAUwzEzs5FUCv2IuBIR9wBtFFftIz3cvNE8fYxQb3S+7RHREREdLS0tVbpoZmYVjOrunYj4LvAExVz8+TRlQ1peSM0GgPml3dqAs6ne1qBuZmY1qXL3ToukO9P6dOA+4JvAXqArNesC9qT1vUCnpGmSFlJ8YHsoTQFdlLQ83bWzrrSPmZnVYGqFNvOAnekOnFuA3oj4tKSDQK+k9cBpYC1ARByV1AscAy4DGyPiSjrWBmAHMB3Yl15mZlaTpqEfEd8AfrxB/XlgxTD79AA9Dep9wEifB5iZ2Tjyb+SamWXEoW9mlhGHvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRqo8OWu+pL+WdFzSUUkfTPVZkvZLOpmWM0v7bJbUL+mEpJWl+lJJR9K2LekJWmZmVpMqV/qXgV+LiB8DlgMbJS0CNgEHIqIdOJDek7Z1AospnqW7NT11C2Ab0E3xCMX2tN3MzGrSNPQj4lxEfDWtXwSOA63AamBnarYTWJPWVwO7I+JSRJwC+oFl6eHpMyLiYEQEsKu0j5mZ1WBUc/qSFlA8OvEpYG562DlpOSc1awXOlHYbSLXWtD60bmZmNakc+pJeBXwK+JWIeGGkpg1qMUK90bm6JfVJ6hscHKzaRTMza6JS6Et6BUXg/2lEPJbK59OUDWl5IdUHgPml3duAs6ne1qB+jYjYHhEdEdHR0tJSdSxmZtZElbt3BPwhcDwifqe0aS/Qlda7gD2leqekaZIWUnxgeyhNAV2UtDwdc11pHzMzq8HUCm3eCvw8cETS06n2YeBhoFfSeuA0sBYgIo5K6gWOUdz5szEirqT9NgA7gOnAvvQyM7OaNA39iPgyjefjAVYMs08P0NOg3gcsGU0Hzcxs7Pg3cs3MMuLQNzPLiEPfzCwjDn0zs4w49M3MMuLQNzPLiEPfzCwjDn0zs4w49M3MMuLQNzPLiEPfzCwjDn0zs4w49M3MMuLQNzPLiEPfzCwjDn0zs5eZBZs+M27HrvK4xE9IuiDpmVJtlqT9kk6m5czSts2S+iWdkLSyVF8q6UjatiU9MtHMzGpU5Up/B7BqSG0TcCAi2oED6T2SFgGdwOK0z1ZJU9I+24Buimfmtjc4ppmZjbOmoR8RXwS+PaS8GtiZ1ncCa0r13RFxKSJOAf3AMknzgBkRcTAiAthV2sfMzGpyvXP6cyPiHEBazkn1VuBMqd1AqrWm9aH1hiR1S+qT1Dc4OHidXTQzs6HG+oPcRvP0MUK9oYjYHhEdEdHR0tIyZp0zM8vd9Yb++TRlQ1peSPUBYH6pXRtwNtXbGtTNzKxG1xv6e4GutN4F7CnVOyVNk7SQ4gPbQ2kK6KKk5emunXWlfczMrCZTmzWQ9EngXmC2pAHgIeBhoFfSeuA0sBYgIo5K6gWOAZeBjRFxJR1qA8WdQNOBfellZmY1ahr6EfGeYTatGKZ9D9DToN4HLBlV78zMbEz5N3LNzDLi0Dczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OM1B76klZJOiGpX9Kmus9vZpazWkNf0hTg94H7gUXAeyQtqrMPZmY5q/tKfxnQHxHPRsQPgN3A6pr7YDYmFmz6zER3wWzUmj4jd4y1AmdK7weAtwxtJKkb6E5vX5R04jrPN1uP8Nx17jtZzQaPuS56ZCLOCuT355zbeNEjNzzm1zUq1h36alCLawoR24HtN3wyqS8iOm70OJOJx5yH3Mac23hh/MZc9/TOADC/9L4NOFtzH8zMslV36P8t0C5poaRbgU5gb819MDPLVq3TOxFxWdL7gb8CpgCfiIij43jKG54imoQ85jzkNubcxgvjNGZFXDOlbmZmNyn/Rq6ZWUYc+mZmGbkpQr/ZVzuosCVt/4akN09EP8dKhfH+mzTOb0j6iqS7J6KfY6nq13dI+heSrkh6d539Gw9VxizpXklPSzoq6W/q7uNYq/B3+9WS/kLS19OY3zsR/Rwrkj4h6YKkZ4bZPvbZFRGT+kXxgfDfA68HbgW+Diwa0uYBYB/F7wksB56a6H6P83h/EpiZ1u+fzOOtOuZSuy8Afwm8e6L7XcOf853AMeC16f2cie53DWP+MPBIWm8Bvg3cOtF9v4Exvx14M/DMMNvHPLtuhiv9Kl/tsBrYFYUngTslzau7o2Ok6Xgj4isR8Z309kmK34eYzKp+fccvA58CLtTZuXFSZcw/BzwWEacBImKyj7vKmAO4Q5KAV1GE/uV6uzl2IuKLFGMYzphn180Q+o2+2qH1OtpMFqMdy3qKK4XJrOmYJbUC/xr4WI39Gk9V/pzfCMyU9ISkw5LW1da78VFlzB8FfozilzqPAB+MiB/W070JMebZVffXMIyHKl/tUOnrHyaJymOR9A6K0P+pce3R+Ksy5o8AH4qIK8VF4KRXZcxTgaXACmA6cFDSkxHxd+PduXFSZcwrgaeBdwJvAPZL+lJEvDDOfZsoY55dN0PoV/lqh5vp6x8qjUXSPwc+DtwfEc/X1LfxUmXMHcDuFPizgQckXY6I/1FLD8de1b/Xz0XES8BLkr4I3A1M1tCvMub3Ag9HMeHdL+kU8CbgUD1drN2YZ9fNML1T5asd9gLr0ifhy4HvRcS5ujs6RpqOV9JrgceAn5/EV31lTcccEQsjYkFELAAeBf79JA58qPb3eg/wNklTJd1O8Y21x2vu51iqMubTFP+yQdJc4C7g2Vp7Wa8xz65Jf6Ufw3y1g6RfSts/RnE3xwNAP/B9iquFSanieH8DeA2wNV35Xo5J/A2FFcd8U6ky5og4LumzwDeAHwIfj4iGt/5NBhX/nP8zsEPSEYqpjw9FxKT9ymVJnwTuBWZLGgAeAl4B45dd/hoGM7OM3AzTO2ZmVpFD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OM/F8hOGhyNzb1ZAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kNQaCJAkwECRJjYEgSQIMBElSM+5ASLI8yf9IsifJ7iQfaOVnJbkzyaPtfWFPm+uS7E3ySJJLe8ovSrKr1d2QJIMNS5J0vAY5QjgM/EpVvQ5YDVybZBWwEdheVSuB7e0zrW4dcD6wBrgxyZy2rZuADcDK9lozQL8kSeMw7kCoqv1V9Zdt+RCwB1gKrAW2tNW2AJe35bXArVX1YlU9DuwFLk6yBJhfVfdUVQG39LSRJE2SCTmHkGQF8AbgPuCcqtoPXWgAZ7fVlgJP9jTb18qWtuXh5SPtZ0OSHUl2HDx4cCK6LklqBg6EJGcAnwV+qaqeH2vVEcpqjPKjC6s2V9VQVQ0tXrz4+DsrSRrVQIGQ5EfowuCPqupzrfjpNg1Eez/QyvcBy3uaLwOeauXLRiiXJE2iQa4yCvApYE9V/XZP1TZgfVteD9zeU74uyWlJzqM7eXx/m1Y6lGR12+ZVPW0kSZNk7gBt3wT8HLAryYOt7EPAR4CtSa4GngCuBKiq3Um2Ag/TXaF0bVW91NpdA9wMzAPuaC9J0iQadyBU1VcZef4f4JJR2mwCNo1QvgO4YLx9kSQNzjuVJUmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEjCNAiHJmiSPJNmbZONU90eSZptpEQhJ5gC/B7wdWAX8qySrprZXkjS7TItAAC4G9lbVY1X1feBWYO0U90kalxUb/2yqu6CT2In8+5p7wrZ8fJYCT/Z83ge8cfhKSTYAG9rH7yZ5ZJz7W5TreWacbWeqReCYJ0uun4q9Av47zwq5fqAx//hoFdMlEDJCWR1VULUZ2DzwzpIdVTU06HZmEsc8Ozjm2eFEjXm6TBntA5b3fF4GPDVFfZGkWWm6BML/AlYmOS/JqcA6YNsU90mSZpVpMWVUVYeTvA/478Ac4NNVtfsE7nLgaacZyDHPDo55djghY07VUVP1kqRZaLpMGUmSppiBIEkCTvJAONbjMNK5odV/I8mFU9HPidTHmP91G+s3knwtyeunop8Tqd/HniT5J0leSnLFZPbvROhnzEnemuTBJLuT/Plk93Ei9fF3/bIkn0/y9Tbed09FPydSkk8nOZDkoVHqJ/77q6pOyhfdyem/Bl4BnAp8HVg1bJ3LgDvo7oNYDdw31f2ehDH/JLCwLb99Noy5Z70vA18Arpjqfk/Cv/MC4GHg3Pb57Knu9wke74eA69vyYuBbwKlT3fcBx/0W4ELgoVHqJ/z762Q+QujncRhrgVuqcy+wIMmSye7oBDrmmKvqa1X1XPt4L909HzNZv489+UXgs8CByezcCdLPmN8JfK6qngCoqpk87n7GW8CZSQKcQRcIhye3mxOrqr5CN47RTPj318kcCCM9DmPpONaZSY53PFfT/cKYyY455iRLgX8BfGIS+3Ui9fPv/GpgYZK7k+xMctWk9W7i9TPejwOvo7uhdRfwgar6weR0b8pM+PfXtLgP4QTp53EYfT0yYwbpezxJfpouEH7qhPboxOtnzB8DPlhVL3U/IGe8fsY8F7gIuASYB9yT5N6q+qsT3bkToJ/xXgo8CPwM8ErgziR/UVXPn+C+TaUJ//46mQOhn8dhnGyPzOhrPEn+EfBJ4O1V9ewk9e1E6WfMQ8CtLQwWAZclOVxV/21Sejjx+v3bfqaqXgBeSPIV4PXATAyEfsb7buAj1U2u703yOPBa4P7J6eKUmPDvr5N5yqifx2FsA65qZ+tXA9+pqv2T3dEJdMwxJzkX+BzwczP01+JwxxxzVZ1XVSuqagVwG/BvZ3AYQH9/27cDb04yN8npdE8P3jPJ/Zwo/Yz3CbqjIZKcA7wGeGxSezn5Jvz766Q9QqhRHoeR5Bda/Sforji5DNgLfI/uV8aM1eeYfw14OXBj+8V8uGbwkyL7HPNJpZ8xV9WeJF8EvgH8APhkVY14+eJ01+e/8X8Ebk6yi24q5YNVNaMfiZ3kM8BbgUVJ9gEfBn4ETtz3l4+ukCQBJ/eUkSTpOBgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElS8/8A0ZcS5KRIZ7kAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Solution'''\n",
    "#take the median over the timesteps\n",
    "X_med=np.median(X, axis=1)\n",
    "\n",
    "for f in range(X_med.shape[1]):\n",
    "    plt.hist(X_med[:, f],bins=300)\n",
    "    plt.title('feature: ' + str(feature_names[f]))\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "interstate-shopper",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Solution'''\n",
    "example_alive=np.where(y==0)[0][0]\n",
    "example_mort=np.where(y==1)[0][0]\n",
    "\n",
    "\n",
    "plt.plot(X[example_alive, :, feature_names=='Heart Rate'].squeeze(), 'o-', label='subject 1 (alive)')\n",
    "plt.plot(X[example_mort, :, feature_names=='Heart Rate'].squeeze(), 'o-', label='subject 2 (mortality)')\n",
    "\n",
    "plt.ylabel('Heart Rate')\n",
    "plt.xlabel('Time step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "settled-platinum",
   "metadata": {},
   "source": [
    "## 2. Task: Generate summary features & time-series specific features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "level-internet",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new fetaure matrix shape (mean):\n",
      "X data (9622, 59)\n",
      "new fetaure matrix shape (mean + sd):\n",
      "X data (9622, 118)\n",
      "new fetaure matrix shape (mean + slope):\n",
      "X data (9622, 118)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-5-d99e8a4aef53>:9: RuntimeWarning: invalid value encountered in divide\n",
      "  X_delta=(X[:, -1, :]-X[:, 0, :])/np.std(X, axis=1)\n"
     ]
    }
   ],
   "source": [
    "'''feature extraction'''\n",
    "#the mean\n",
    "X_m=np.mean(X, axis=1)\n",
    "\n",
    "#the variance\n",
    "X_sd=np.std(X, axis=1)\n",
    "\n",
    "#the delta\n",
    "X_delta=(X[:, -1, :]-X[:, 0, :])/np.std(X, axis=1)\n",
    "X_delta[np.isnan(X_delta)]=0\n",
    "\n",
    "#(1) mean feature value\n",
    "X_data_1=X_m\n",
    "feature_names_1=feature_names\n",
    "print('new fetaure matrix shape (mean):\\nX data', X_data_1.shape)\n",
    "\n",
    "'''hint: concatonate the features together into a new feature matrix and create a new vector with the feature names (i.e. the mean, the variance, etc.)'''\n",
    "#(2) mean feature value + variability of feature\n",
    "X_data_2=np.concatenate((X_m, X_sd), axis=1)\n",
    "feature_names_2=np.concatenate((feature_names + '(mean)', feature_names + '(std)'), axis=0)\n",
    "print('new fetaure matrix shape (mean + sd):\\nX data', X_data_2.shape)\n",
    "\n",
    "#(3) mean feature value + slope of feature\n",
    "X_data_3=np.concatenate((X_m, X_delta), axis=1)\n",
    "feature_names_3=np.concatenate((feature_names + '(mean)', feature_names + '(delta)'), axis=0)\n",
    "print('new fetaure matrix shape (mean + slope):\\nX data', X_data_3.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "animated-florence",
   "metadata": {},
   "source": [
    "## Task: Implement stratified k-fold cross validation\n",
    "Split the data into training and validation, test sets\n",
    "- <b>Hint</b>, get the pipleine running with one split only, they merge your pipeline into a loop\n",
    "- the code below demonstrates how to ranomly split the data (non k-fold)\n",
    "- Either manually create a k-fold validation split, or use ScikitLearn's functions:\n",
    "https://scikit-learn.org/stable/modules/cross_validation.html#stratification\n",
    "- <b>Hint</b>: make sure the distributions are stratifed between train, validation and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "thrown-pepper",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''example of making one split, using mean features'''\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_data_1, y, test_size=0.33, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "banner-eugene",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'task: implement stratified k-fold cross validation'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''task: implement stratified k-fold cross validation'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "approximate-trinity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'solution: implement stratified k-fold cross validation'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''solution: implement stratified k-fold cross validation'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ac92069d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (7697, 59)\n",
      "y_train shape: (7697,)\n",
      "---------------------------------\n",
      "X_train shape: (7697, 59)\n",
      "y_train shape: (7697,)\n",
      "---------------------------------\n",
      "X_train shape: (7698, 59)\n",
      "y_train shape: (7698,)\n",
      "---------------------------------\n",
      "X_train shape: (7698, 59)\n",
      "y_train shape: (7698,)\n",
      "---------------------------------\n",
      "X_train shape: (7698, 59)\n",
      "y_train shape: (7698,)\n",
      "---------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "import numpy as np\n",
    "\n",
    "# Assuming X_data_1 and y are defined, and y is categorical\n",
    "skf = StratifiedKFold(n_splits=5)\n",
    "\n",
    "for train_index, test_index in skf.split(X_data_1, y):\n",
    "    X_train, X_test = X_data_1[train_index], X_data_1[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "    \n",
    "    # Check shapes\n",
    "    print(\"X_train shape:\", X_train.shape)\n",
    "    print(\"y_train shape:\", y_train.shape)\n",
    "    print('---------------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "adopted-diving",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "Distribution : n=  7698\n",
      "0: alive     : n=  6381 (82.89%)\n",
      "1: mortality : n=  1317 (17.11%)\n",
      "Testing\n",
      "Distribution : n=  1924\n",
      "0: alive     : n=  1595 (82.90%)\n",
      "1: mortality : n=   329 (17.10%)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Counter({0: 1595, 1: 329})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''helper function to calculate class distributions'''\n",
    "import collections\n",
    "def calculate_class_distributions(labels, numeric_labels, response):\n",
    "    \n",
    "    counter=collections.Counter(sorted(response))\n",
    "    counts=np.array(list(counter.values()))\n",
    "    keys=np.array(list(counter.keys()))\n",
    "\n",
    "    print('Distribution : n={:6.0f}'.format(len(response)))\n",
    "    for ix, label in enumerate(labels):\n",
    "        print('{}: {:10s}: n={:6.0f} ({:3.2f}%)'.format(keys[ix], label, counts[ix], counts[ix]/len(response)*100))\n",
    "\n",
    "    return counter\n",
    "\n",
    "print('Training')\n",
    "calculate_class_distributions(labels=['alive','mortality'], numeric_labels=[0, 1], response=y_train)\n",
    "print('Testing')\n",
    "calculate_class_distributions(labels=['alive','mortality'], numeric_labels=[0, 1], response=y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "constant-bidder",
   "metadata": {},
   "source": [
    "We have uneven distributions of alive after timestep $T+1$ versus a mortality, we therefore have to consider modeling this as an imbalanced data problem;\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "trying-anger",
   "metadata": {},
   "source": [
    "## 3. Task: Apply the necessay pre-processing steps \n",
    "    - normalisation\n",
    "    - outlier removal: mean imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "printable-connecticut",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''task: normalisation'''\n",
    "def zscore_data(x):\n",
    "    '''...write a function to standardise the data using the zscore'''\n",
    "    return x \n",
    "\n",
    "X_test=zscore_data(X_test)\n",
    "X_train=zscore_data(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "constitutional-casino",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"alternative method of normalising, using ScikitLearn's function\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''solution: normalisation'''\n",
    "def zscore_data(x):\n",
    "    '''...write a function to standardise the data using the zscore'''\n",
    "    x_=x.copy()\n",
    "    for i in range(x.shape[1]):\n",
    "        x_[:, i]=(x_[:,i]-np.mean(x_[:, i]))/np.std(x_[:, i])\n",
    "    x=x_\n",
    "    return x \n",
    "\n",
    "X_test=zscore_data(X_test)\n",
    "X_train=zscore_data(X_train)\n",
    "\n",
    "'''alternative method of normalising, using ScikitLearn's function''' \n",
    "#from sklearn.preprocessing import StandardScaler\n",
    "#X_train = StandardScaler().fit_transform(X_train)\n",
    "#X_test = StandardScaler().fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hazardous-eight",
   "metadata": {},
   "source": [
    "Perform outlier removal using the following cases:\n",
    "$$\n",
    "x_i=\\begin{cases}\n",
    "\t\t\t\\bar{\\mathbf{x}}, & \\text{if $x_i$ > $\\bar{\\mathbf{x}} \\pm (\\alpha\\times\\sigma)$}\\\\\n",
    "            x_i, & \\text{otherwise}\n",
    "\t\t \\end{cases}, \\forall \\ \\mathbf{x} \\in \\mathbf{X}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "engaging-composite",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''task: outlier removal'''\n",
    "# - impute as the mean value if the feature value is > mean + threshold*std\n",
    "def remove_outliers(X, sd_threshold=5):\n",
    "    '''...write a function to remove the outliers in the data'''\n",
    "    return X\n",
    "\n",
    "X_train=remove_outliers(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "floral-position",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''solution: outlier removal'''\n",
    "# - impute as the mean value if the feature value is > mean + threshold*std\n",
    "def remove_outliers(X, sd_threshold=5):\n",
    "    \n",
    "    nrem=[]\n",
    "    for f in range(X.shape[1]):\n",
    "        mu=np.mean(X[:, f])\n",
    "        sigma=np.std(X[:, f])\n",
    "        threshold= + sd_threshold*sigma\n",
    "        \n",
    "        idx=X[:, f] > threshold\n",
    "        X[idx]=mu\n",
    "        nrem.append(sum(idx))\n",
    "     \n",
    "    nrem=np.array(nrem)\n",
    "    return X, nrem\n",
    "\n",
    "X_train, nrem=remove_outliers(X_train)\n",
    "X_test, _=remove_outliers(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nearby-effectiveness",
   "metadata": {},
   "source": [
    "## Task: Train and compare two off-the-shelf machine learning models (linear model)\n",
    "In this section we will now train our off-the-shelf machine leanring (ML) model. A great starting point is to use a simple linear model, such as logistic regression, which essentially describes a linear regression with a (non-linear) logistic output to perform classification. \n",
    "\n",
    "- You are free to use any machine learning model you wish, or compare between models\n",
    "\n",
    "### Background\n",
    "\n",
    "#### Linear Regression\n",
    "A linear regression model explicitly describes a relationship between predictor(s) $\\mathbf{X} \\in \\mathbb{R}^{N\\times P}$ and continuous response variables $\\mathbf{y} \\in \\mathbb{R}^{N}$. For an $i^{th}$ observation row of $\\mathbf{X}$, $\\mathbf{x}\\equiv\\mathbf{x_i}\\in\\mathbb{R}^{1\\times P}$:\n",
    "\\begin{align}\n",
    "        \\hat{y} &=w_0 + w_1x_1 + w_2x_2+\\dotsc+w_Px_P + \\epsilon\\\\\n",
    "        &=w_0 + \\sum_{j=1}^Pw_jx_j + \\epsilon\\\\\n",
    "        &=\\mathbf{w}^\\top\\mathbf{x} + b \\\\\n",
    "        &=\\mathbf{w}^\\top\\mathbf{x}\n",
    "\\end{align}\n",
    "where $w_j$ values denote the slope (weights, or regression coefficients) of the $x_j$ features; $w_0$ is the intercept term; and $\\epsilon$ denote the residual (model) errors term, which are assumed to be normally distributed with constant variance, $\\epsilon\\sim\\mathcal{N}(0,\\sigma^2)$ [1,2-3]. Often a linear model is described in vector notation, $\\mathbf{w}=[w_1, w_2, ..., w_P]$, where $w_0$ is denoted as the <em>bias</em>, $b$ term, and the $\\epsilon$-term is often omitted. More succinctly, a linear model can be also represented by the equation $\\mathbf{w}^\\top\\mathbf{x}$, where $\\mathbf{w}=[w_0, w_1,w_2,...,w_{P+1}]$ is a vector of regression coefficients, including $w_0$ (or $b$) as the first value and $\\mathbf{x}\\in\\mathbb{R}^{1\\times(P+1)}$ which is first concatenated with a vector column of ones to account for $w_0$ (or $b$) in $\\mathbf{w}$ at the first index. \n",
    "\n",
    "#### Logistic Regression\n",
    " Generalised linear models (GLMs) are extensions of linear regression models that can have non-linear outputs [2]. GLMs utilise canonical link functions, $\\phi$, to transform the outputs of a linear regression:  $\\varphi=\\mathbf{w}^\\top\\mathbf{x}$ to another distribution, such as with a logistic $\\phi = \\sigma(\\varphi)$ link function (or inversely the logit, representing the log-odds) which will be used to form Logistic Regression for binary classification tasks [1,2]:\n",
    " \\begin{equation}\n",
    "     \\sigma(x)=\\frac{1}{1+e^{-x}}\n",
    " \\end{equation}\n",
    " in this case $\\phi$ is sigmoidal and is bounded between $[0,1]$, therefore the output of $\\sigma$ can be interpreted as the probability of $y=1$: \n",
    " \\begin{equation}\n",
    "    p(\\mathbf{x}; \\mathbf{w})=\\frac{1}{1+e^{-(\\mathbf{w^\\top}\\mathbf{x})}}\n",
    "\\end{equation}\n",
    "A threshold can be applied to the probabilistic output $p$ to determine a classification prediction $\\hat{y}$ for a Logistic Regression model; threshold values are typically chosen as 0.5, but this can be altered based on the use case.  \n",
    "\n",
    "#### Regularisation $\\rightarrow$ Feature Selection\n",
    "Many statistical and machine learning models can easily overfit to the training data, resulting in poorer estimations, models that are not generalisable or too complex. Regularisation is often introduced to mitigate against this. For example large coefficient values in a regression can be penalised by adding a regularisation term to a loss function, or through reducing the number of parameters or features used in a model. The most common regularisers use the $\\ell_p$-norm defined by [1,5]:\n",
    "\n",
    "$$\n",
    "   ||\\mathbf{x}||_p=\\left(\\sum_{i=1}^N|x_i|^p\\right)^{1/p}\n",
    "$$ \n",
    "for any $\\mathbf{x}\\in\\mathbb{R}^{N\\times1}$, where the real number $p\\geq1$ defines the $\\ell_p$ space.\n",
    "\n",
    "#### LASSO for Logistic Regression\n",
    "The Least Absolute Shrinkage and Selection Operator (LASSO) [5-6] is a technique that conversely solves the  $\\ell_1\\mathrm{-penalised}$ sum of squares in a linear regression such that:\n",
    "\n",
    "$$\n",
    "    \\hat{\\mathbf{w}} =\\underset{\\mathbf{w}}{\\operatorname{argmax}}\\left\\{\\sum_{i=1}^N|y_i-w_0\\sum_{j=1}^{P}w_{j}x_{ij}|^2 + \\lambda\\sum_{j=1}^P|w_j|\\right\\}\n",
    "$$\n",
    "\n",
    "This is equivalent to minimising the sum of squares with a constraint of the form: $||w||_1 = \\sum_{j}^N|w_j|\\leq t$. \n",
    "Because of the form of the $\\ell_1$-penalty, LASSO both shrinks coefficients but also encourages sparsity in a model's parameters and thus inherently forms feature selection, shrinking non-important features to zero. \n",
    "\n",
    "The LASSO can also be extended to perform feature selection for classification by substituting a canonical link function (such as the logistic $\\phi=\\sigma(\\varphi)$ and following the same procedure, essentially performing regularised-logistic regression [3]).\n",
    "\n",
    "LASSO regularisation for logistic regression can therefore be employed in order to reduce the dimensions of the extracted feature space into a ranked parsimonious set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "offensive-moderator",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                   random_state=42, solver=&#x27;saga&#x27;, tol=0.001)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.01, class_weight=&#x27;balanced&#x27;, penalty=&#x27;l1&#x27;,\n",
       "                   random_state=42, solver=&#x27;saga&#x27;, tol=0.001)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=0.01, class_weight='balanced', penalty='l1',\n",
       "                   random_state=42, solver='saga', tol=0.001)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''run logistic regression with l1 regularisation (i.e. LASSO)'''\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "LAMBDA=0.01 \n",
    "\n",
    "clf_l1_LR=LogisticRegression(random_state=42, penalty='l1', C=LAMBDA, solver=\"saga\", tol=0.001, class_weight='balanced')\n",
    "clf_l1_LR.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "military-associate",
   "metadata": {},
   "source": [
    "<b>hint</b> use the ``class_weight='balanced'`` parameter to account for imbalanced data in the loss fucntion. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fourth-vietnamese",
   "metadata": {},
   "source": [
    "## Ancillary task: \n",
    "- optimse over $\\lambda$ values (i.e. parameter C), using internal cross-validation using training and validation sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "solar-departure",
   "metadata": {},
   "source": [
    "## Task: Train and compare two off-the-shelf machine learning models (non-linear model)\n",
    "\n",
    "### Random Forest\n",
    "Classification and Regression Trees (CART) specifically Random Forests (RF) are a multi-functional, non-linear method capable of performing regression, classification and feature selection [1,4]. Unlike the linear filter-based methods of feature selection introduced thus far, for example, LASSO (which are also often used to (linearly) pre-select features prior to application in (non-linear) methods, such as SVMs), RFs incorporate non-linear feature selection as part of the model methodology. \n",
    "\n",
    "*Below is a simple diagram describing the construction of a Random Forest (RF) model.*\n",
    "  ![./RF_schematic.png](RF_schematic.png)\n",
    "\n",
    "Random Forests consist of a large ensemble of decision trees arranged in a hierarchical structure, as depicted in figure \\ref{fig:methods:rf}. To build an individual tree, we recursively descend through the hierarchy, performing binary splits (decisions) at each level in the structure (a node, $j$) using a single feature $x_j\\in\\mathcal{X}^p$ based on a threshold value (splitting criterion) $s_j$, sub-partitioning the feature space $\\mathcal{X}_j$ at each node. A tree is typically expanded until all leaves are pure (i.e each partition $\\mathcal{X}_j$ represents only one class) or until all leaves contain less than the minimum number of samples in a partition $\\mathcal{X}_j$ required to split a node. While decision trees are susceptible to over-fitting, the advantage of a RF is that multiple trees can be learned, introducing a variability between the trees. An individual tree selects $m<N$ random subset of observations (with replacement), and each node considers a random subset of $p$ features for each split. Importantly, the same feature can be selected for multiple nodes in the tree, and can have different associated $s_j$ values at each node. Once all trees have been grown, each of the <em>weaker</em> decisions are aggregated (or \\textit{ensembled}) creating a robust final prediction. \n",
    "Classification predictions are deduced as the majority class label of the observations present in each final partition $\\mathcal{X}_j$, whereas for continuous prediction (i.e. regression) the mean of the (continuous) responses would be calculated instead. To determine the optimal split criterion $s_j$ for each $x_j$ to create each $\\mathcal{X}_j$ we evaluate the \\textit{Gini} importance, which quantifies the average gain of purity (i.e. the presence of one class) caused by splits of a given variable.\n",
    "RFs have relatively little hyperparamater tuning, which typically only include the number of trees to build ($k=1500$ in this thesis) and the number of input variables chosen at each node ($p$). Values of $p$ are suggested in [8] are: $p \\in \\{\\sqrt{P}, 2\\sqrt{P}, \\sqrt{P}/2 \\}$.\n",
    "\n",
    "### Balanced Random Forest\n",
    "In this example we utalise a balanced Random Forest: `BalancedRandomForestClassifier`, which randomly under-samples each boostrap sample to balance the classes during training. For more details see: https://imbalanced-learn.org/stable/references/generated/imblearn.ensemble.BalancedRandomForestClassifier.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "binding-application",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.9s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    2.1s\n",
      "[Parallel(n_jobs=4)]: Done 792 tasks      | elapsed:    3.7s\n",
      "[Parallel(n_jobs=4)]: Done 1242 tasks      | elapsed:    5.8s\n",
      "[Parallel(n_jobs=4)]: Done 1792 tasks      | elapsed:    8.4s\n",
      "[Parallel(n_jobs=4)]: Done 2000 out of 2000 | elapsed:    9.3s finished\n"
     ]
    },
    {
     "data": {
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       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BalancedRandomForestClassifier(n_estimators=2000, n_jobs=4, oob_score=True,\n",
       "                               random_state=42, replacement=True,\n",
       "                               sampling_strategy=&#x27;not minority&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BalancedRandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>BalancedRandomForestClassifier(n_estimators=2000, n_jobs=4, oob_score=True,\n",
       "                               random_state=42, replacement=True,\n",
       "                               sampling_strategy=&#x27;not minority&#x27;, verbose=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "BalancedRandomForestClassifier(n_estimators=2000, n_jobs=4, oob_score=True,\n",
       "                               random_state=42, replacement=True,\n",
       "                               sampling_strategy='not minority', verbose=1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''balanced Random Forest'''\n",
    "from imblearn.ensemble import BalancedRandomForestClassifier\n",
    "\n",
    "clf_RF = BalancedRandomForestClassifier(\n",
    "    n_estimators=2000,\n",
    "    replacement=True,\n",
    "    sampling_strategy='not minority',\n",
    "    oob_score=True,\n",
    "    n_jobs=4,\n",
    "    random_state=42,\n",
    "    verbose=1\n",
    ")\n",
    "clf_RF.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ranging-sheffield",
   "metadata": {},
   "source": [
    "## Task: Evaluate the model(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "signal-eating",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''helper functions to compute evluation metrics'''\n",
    "import sklearn.metrics as metrics\n",
    "\n",
    "def get_one_hot(targets, nb_classes):\n",
    "    '''numpy version'''\n",
    "    res = np.eye(nb_classes)[np.array(targets).reshape(-1)]\n",
    "    return res.reshape(list(targets.shape)+[nb_classes])\n",
    "\n",
    "def compute_scores(y_true, y_pred, y_prob=None):\n",
    "    ''' Compute a bunch of scoring functions '''\n",
    "    auc=np.nan\n",
    "    confusion = metrics.confusion_matrix(y_true, y_pred)\n",
    "    per_class_recall = metrics.recall_score(y_true, y_pred, average=None)\n",
    "    accuracy = metrics.accuracy_score(y_true, y_pred)\n",
    "    f1=metrics.f1_score(y_true, y_pred, pos_label=1)\n",
    "    balanced_acuracy = metrics.balanced_accuracy_score(y_true, y_pred)\n",
    "    kappa = metrics.cohen_kappa_score(y_true, y_pred)\n",
    "    if y_prob is not None:\n",
    "        auc=metrics.roc_auc_score(get_one_hot(y_true,  y_prob.shape[1]), y_prob)\n",
    "    return {\n",
    "        'confusion': confusion,\n",
    "        'per_class_recall': per_class_recall,\n",
    "        'accuracy': accuracy,\n",
    "        'balanced_accuracy': balanced_acuracy,\n",
    "        'kappa': kappa,\n",
    "        'F1': f1, \n",
    "        'AUROC': auc,\n",
    "    }\n",
    "\n",
    "def print_scores(scores):\n",
    "    print('Accuracy: {:.3f} | Balanced Accuracy: {:.3f} | AUROC: {:.3f} | Kappa: {:.3f} | F1: {:.3f}'.format(scores['accuracy'], scores['balanced_accuracy'], scores['AUROC'], scores['kappa'], scores['F1']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collect-juice",
   "metadata": {},
   "source": [
    "#### (1) Evaluate LASSO-Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "miniature-crowd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logstic Regression\n",
      "Accuracy: 0.636 | Balanced Accuracy: 0.695 | AUROC: 0.725 | Kappa: 0.233 | F1: 0.424\n"
     ]
    }
   ],
   "source": [
    "'''evalute the logistic regression model'''\n",
    "y_train_pred = clf_l1_LR.predict(X_train)\n",
    "y_test_pred = clf_l1_LR.predict(X_test)\n",
    "y_test_prob = clf_l1_LR.predict_proba(X_test) \n",
    "\n",
    "print('Logstic Regression')\n",
    "print_scores(compute_scores(y_test, y_test_pred, y_test_prob))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "controlling-bullet",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normalised confusion matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "labelstr=['alive', 'mortality']\n",
    "CM=confusion_matrix(y_test,y_test_pred, )\n",
    "nCM=CM/sum(CM[:])\n",
    "\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=CM,\n",
    "                    display_labels=labelstr)\n",
    "print('confusion matrix')\n",
    "disp.plot();plt.show()\n",
    "\n",
    "ndisp = ConfusionMatrixDisplay(confusion_matrix=nCM,\n",
    "                    display_labels=labelstr)\n",
    "print('normalised confusion matrix')\n",
    "ndisp.plot(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "artificial-belief",
   "metadata": {},
   "source": [
    "#### (2) Evaluate Balanced Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "consistent-given",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 792 tasks      | elapsed:    0.3s\n",
      "[Parallel(n_jobs=4)]: Done 1242 tasks      | elapsed:    0.5s\n",
      "[Parallel(n_jobs=4)]: Done 1792 tasks      | elapsed:    0.7s\n",
      "[Parallel(n_jobs=4)]: Done 2000 out of 2000 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 792 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=4)]: Done 1242 tasks      | elapsed:    0.7s\n",
      "[Parallel(n_jobs=4)]: Done 1792 tasks      | elapsed:    1.0s\n",
      "[Parallel(n_jobs=4)]: Done 2000 out of 2000 | elapsed:    1.1s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 792 tasks      | elapsed:    0.3s\n",
      "[Parallel(n_jobs=4)]: Done 1242 tasks      | elapsed:    0.6s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "Accuracy: 0.679 | Balanced Accuracy: 0.717 | AUROC: 0.751 | Kappa: 0.278 | F1: 0.453\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done 1792 tasks      | elapsed:    0.8s\n",
      "[Parallel(n_jobs=4)]: Done 2000 out of 2000 | elapsed:    0.9s finished\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = clf_RF.predict(X_train)\n",
    "y_test_pred = clf_RF.predict(X_test)\n",
    "y_test_prob = clf_RF.predict_proba(X_test) \n",
    "\n",
    "print('Random Forest')\n",
    "print_scores(compute_scores(y_test, y_test_pred, y_test_prob))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "clear-heavy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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Z2eVg+FtjL2P9TGP7ImJKZYpkZla6ctWYq6mxGvPljewL4NAylyWz/jFtM47cemi1i2FNMPDAD6tdBGuiN8p1ooxPUFSMxgaYHLIpC2Jm1mwt4LVRxShqgImZWYvhwGxmli3KwUT5Dsxmli85qDEXMyRbkr4i6Ufp9raS9ql80czMmkZR/JJlxQzJvgrYF/hSur0c+G3FSmRm1hw5eLVUMU0Zn42Iz0h6ESAilqRDFM3MsifjteFiFBOY16Yz8QeApN7k4j20ZpZHWW+mKEYxgfnXwB1AH0ljSGab+4+KlsrMrBTRSnplRMRNkl4gmbhDwIiIeKXiJTMzK0VrqDFL2hb4ALi7MC0i3q5kwczMStIaAjPJG7HrXsraCRgEvEbyNlgzs0xpFW3MEbF74XY669xZDWQ3M7NmavLIv4iYImnvShTGzKzZWkONWdJ3CjbbkLyQcGHFSmRmVqrW0isD6Fqwvo6kzfn2yhTHzKyZ8l5jTgeWdImI722i8piZlUzk/OGfpHYRsa6xV0yZmWVOngMzyZuwPwNMlTQBuBVYWbczIv5a4bKZmTVNC5g5rhjFtDH3AN4jecdfXX/mAByYzSx7cv7wr0/aI+MlPg7IdXLwnWRmeZT3GnNboAufDMh1cnDrZpZLOYhOjQXmeRHx401WEjOz5moFb8nO9hT/Zmb1yHtTxmGbrBRmZuWS58AcEYs3ZUHMzMqhtQzJNjNrGVpBG7OZWYsi8vFwzIHZzPLFNWYzs2zJe68MM7OWx4HZzCxDcjJRfptqF8DMrKyiyKUIktpKelHSPel2D0kPS3o9/dyyIO9FkmZKek3Skc25BQdmM8sVRXFLkb4FvFKwfSEwMSIGAxPTbSQNAU4FdgWOAq5KXzRSEgdmM8uXMtWYJfUHjgWuLUg+Hhibro8FRhSkj4uI1RExC5gJ7FPqLTgwm1muNKHG3EvS8wXLmeud6lfA9/nkDM99I2IeQPrZJ03fBninIN/sNK0kfvhnZvkRNGWi/EURMay+HZK+ACyIiBckHVzEuco6PbIDs5nlRhlfxro/cJykY4BOQDdJNwLzJfWLiHmS+gEL0vyzgQEFx/cH5pZ6cTdlmFm+lKGNOSIuioj+ETGQ5KHepIj4CjABGJVmGwXcla5PAE6V1FHSIGAwyXtTS+Ias5nliqKiI0wuBcZLOgN4GzgFICJeljQemAGsA86NiJpSL+LAbGb5UYHZ5SLiUeDRdP09GpirPiLGAGPKcU0HZjPLFc+VYWaWMXkYku3AbGb54hqzmVmGNG24dWY5MJtZvjgwm5llRxkHmFSVA7OZ5YpqW35kdmA2s/zwW7KtJem/w4f88HdvfbS91bZr+PNlW3HHtb0BOPnsBXz9R/M4ZbddWbbY/yyqpXfPlXz/3L/RY4tV1NaK+ybuxB33D+GrJ7/IMYe9ztJlHQG47ua9eHZqf/r2Xs4ff3kns+d2A+CV13tzxbX7VfMWqs7d5TYRSUOBrSPivo3kOxj4bkR8QdJxwJCIuFTSCOAfETGj0mXNqtn/7MQ3Pr8zAG3aBDdNmcET93cHoPfWa/j0QcuZP7t9NYtoQE2N+P2f92bmrJ507rSWq352Ny9M2xqA2+8dwm337LbBMXPnd+XsHxy/qYuaXTmoMWd+EiNJ7YChwDFNOS4iJkTEpenmCGBIeUvWcg09cAXz3urAgjkdADjrkrn88b+2prJTDFgxFr+/GTNn9QRg1YfteXtOd3r1+KDKpWpZyvwGk6qoWGCWNFDSq5KulfSSpJskHS7pifR9Wfuk78+6U9I0SU9L2iM99hJJ10h6CLgB+DEwUtJUSSPTY59M38X1pKSd67n+aElXStoPOA64LD1+B0lTCvINlvRCpX4OWXTw8Ut49M7kVWXDj1jKonfb88aMzlUula2vb+/l7DhoMa/O7AXA8Ue+wu9/cRcXnP04XTZf/VG+rXqv4OpLJ3D5xfez2y7zq1XcbAggorglwyrdlLEjyexLZwLPAV8GDiAJlD8kmfH/xYgYIelQkiA8ND12L+CAiFglaTQwLCK+CSCpG3BQRKyTdDjwU+Ck+goQEU9KmgDcExG3pccvlTQ0IqYCpwPXr39c+jaDMwE6sVkzfwzZ0a59LcOPWMZ1P+1Hx861fOn8BVz0pe2rXSxbT6eOa/nRdx7l6rH78MGqDtz98C7cdPueBGL0F1/krK8+x+W/O4DFSzbjtHNPZvmKTgwetIhLvjuJr393BB+s6lDtW6iaPLQxV7opY1ZETI+IWuBlkpcYBjAdGEgSpP8MEBGTgJ6SuqfHToiIVQ2ctztwq6SXgP8heQFiU1wLnJ6+LHEk8Jf1M0TENRExLCKGtadjE0+fXXsfupyZ0zvz/qL29NtuNVttu4arH3mNsc/MoHe/tfz2wX+wZe+11S5mq9a2bS0XXzCZSY9vz+PPbgfA+0s7UxttiBD3TRrMzjsuAmDturYsX9EJgNdn9WLe/K7077esamWvtrp+zC29KaPSNebVBeu1Bdu16bXX1XNM3Y9sZSPn/QkwOSJOkDSQdEq+JrgduBiYBLyQTuXXKhw84v2PmjHefLUzI/f4+Dtt7DMzOO/ondwro6qCC85+grfndOf2ez/+3fTY4gMWv5/85bb/3m/z5jtbANC964csX9GB2mjDVn2Ws02/5cyb37UaBc+GFtBMUYxq/w98DDgN+Enao2JRRCyTNnh91nKg8F9bd2BOuj66iOt84viI+FDSg8DVwBmlFLwl6ti5ls8cuJwrvt+/2kWxBuy68wI+f9A/eeOtLfndz5OXY1x3814csv8b7DBwMRFi/sIu/OoP+wKw+6feZdQXp1JTK2prxRV/2JflK/PzF14psl4bLka1A/MlwJ8kTQM+4ONXtqxvMnChpKnAz4BfAGMlfYek1rsx44A/SDofODki/gncBJwIPNSsO2hBVq9qwym7bdjdqs6oz7rjSrW9/FpfPj9y9Abpz06t/8v08WcH8vizAytbqJbGgblhEfEmsFvB9ugG9m3QATMiLllvezGw93rZdipY/88036N8/KaB60kf6kXEE2zYXe4A4LrmvP7FzLLHNeYWStIdwA7AodUui5mVUQA1LT8yt8rAHBEnVLsMZlYZrjGbmWWNe2WYmWWLa8xmZlniaT/NzLJFgPzwz8wsW+Q2ZjOzDHFThplZ1niuDDOzzHGvDDOzrHGN2cwsQ8K9MszMsqflx2UHZjPLF3eXMzPLGgdmM7MMCZIX17VwDsxmlhsi3JRhZpY5tS2/ytym2gUwMyubuqaMYpZGSBogabKkVyS9LOlbaXoPSQ9Lej393LLgmIskzZT0mqQjm3MbDsxmliuKKGrZiHXABRHxKWA4cK6kIcCFwMSIGAxMTLdJ950K7AocBVwlqW2p9+DAbGb5ElHc0ugpYl5ETEnXlwOvANuQvDx6bJptLDAiXT8eGBcRqyNiFjAT2KfUW3BgNrMcKTIoJ4G5l6TnC5Yz6zujpIHAp4FngL4RMQ+S4A30SbNtA7xTcNjsNK0kfvhnZvnRtLdkL4qIYY1lkNQFuB34fxGxTFKDWRsoTUlcYzazXClTGzOS2pME5Zsi4q9p8nxJ/dL9/YAFafpsYEDB4f2BuaXegwOzmeVLGdqYlVSN/wi8EhG/LNg1ARiVro8C7ipIP1VSR0mDgMHAs6XegpsyzCw/AqgtywCT/YGvAtMlTU3TfghcCoyXdAbwNnAKQES8LGk8MIOkR8e5EVFT6sUdmM0sR8rzBpOIeJz6240BDmvgmDHAmGZfHAdmM8sbD8k2M8uQAGpa/pBsB2Yzy5GAcGA2M8sWN2WYmWVI+XplVJUDs5nli2vMZmYZ48BsZpYhEVBT8riOzHBgNrN8cY3ZzCxjHJjNzLIk3CvDzCxTAsIDTMzMMsZDss3MMiQCah2YzcyyxQ//zMyyJVxjNjPLkvJMlF9tDsxmlh+exMjMLFsCCA/JNjPLkPBE+WZmmRNuyjAzy5gc1JgVOXiCWWmSFgJvVbscFdILWFTtQliT5PV3tl1E9G7OCSQ9QPLzKcaiiDiqOderFAfmVk7S8xExrNrlsOL5d5Z/bapdADMz+yQHZjOzjHFgtmuqXQBrMv/Ocs5tzGZmGeMas5lZxjgwm5lljANzKyHpTUm90vUnq10eaxpJQyUdU0S+gyXdk64fJ+nCdH2EpCGVLqeVhwNzKxQR+1W7DFY8Se2AocBGA3OhiJgQEZemmyMAB+YWwoE5hyTdKekFSS9LOrOe/SvSz1sKa2GSrpd0kqS2ki6T9JykaZLO2pTlzwtJAyW9KulaSS9JuknS4ZKekPS6pH0k9Uh/X9MkPS1pj/TYSyRdI+kh4Abgx8BISVMljUyPfVLSi+nnzvVcf7SkKyXtBxwHXJYev4OkKQX5Bkt6YRP9WKwInisjn/4tIhZL6gw8J+n2BvKNA0YC90nqABwGnAOcASyNiL0ldQSekPRQRMzaJKXPlx2BU4AzgeeALwMHkATKHwLvAC9GxAhJh5IE4aHpsXsBB0TEKkmjgWER8U0ASd2AgyJinaTDgZ8CJ9VXgIh4UtIE4J6IuC09fqmkoRExFTgduL7cN26lc2DOp/MlnZCuDwAGN5DvfuDXafA9CngsDQJHAHtIOjnN1z09hwNz082KiOkAkl4GJkZESJoODAS2Iw2oETFJUk9J3dNjJ0TEqgbO2x0YK2kwyTTE7ZtYrmuB0yV9h+TLeZ8mHm8V5MCcM5IOBg4H9o2IDyQ9CnSqL29EfJjuP5LkP+fNdacBzouIBytd3lZgdcF6bcF2Lcn/v3X1HFM3uGBlI+f9CTA5Ik6QNBB4tInluh24GJgEvBAR7zXxeKsgtzHnT3dgSRqUdwGGbyT/OJI/ZQ8E6gLxg8A5ktoDSNpJ0uaVKnAr9xhwGnz0pbooIpbVk2850LVguzswJ10fXcR1PnF8RHxI8nu+GvhTE8tsFebAnD8PAO0kTSOpVT29kfwPAQcBj0TEmjTtWmAGMEXSS8Dv8V9XlXIJMCz9fV0KjGog32RgSN3DP+AXwM8kPQG0LeI644DvpQ8Ld0jTbiKpnT/UnBuw8vOQbLNWStJ3ge4R8Z/VLot9kmtBZq2QpDuAHYBDq10W25BrzGZmGeM2ZjOzjHFgNjPLGAdmM7OMcWC2spBUk3bleknSrZI2a8a5rq8bdZjOM9Hg5DvpbGpNnpSpcLa9YtLXy7Oiide6JO0BYVYUB2Yrl1URMTQidgPWAGcX7pRUTF/bDUTE1yJiRiNZDgY8W57ligOzVcLfgB3T2uxkSX8Bpjc0a50SV0qaIeleoE/diSQ9KmlYun6UpCmS/i5pYjoU+Wzg22lt/UBJvSXdnl7jOUn7p8f2lPRQOsDi9yTDzhulRmbpk3R5WpaJknqnaTtIeiA95m/pyEuzJnM/ZisrJXMHH00yAhGSyXF2i4hZaXDbYNY64NPAzsDuQF+SUYfXrXfe3sAfSGZUmyWpRzqD3u+AFRHx32m+vwD/ExGPS9qWZNjxp0jmhXg8In4s6ViS2d42ZoNZ+tI5JTYHpkTEBZJ+lJ77myQvST07Il6X9FngKtxP2ErgwGzl0lnS1HT9b8AfSZoYni2YLrShWesOAm6OiBpgrqRJ9Zx/OMnsd7MAImJxA+U4nGToct12N0ld02ucmB57r6QlRdxTfbP0vUcyAdEtafqNwF8ldUnv99aCa3cs4hpmG3BgtnJZFRFDCxPSAFU4Q1q9s9Ypmax/YyOdVEQeSJrn9l1/usy0LEWPpmrKLH3pedsA76//MzArhduYbVNqaNa6x4BT0zbofsAh9Rz7FPA5SYPSY3uk6evPuvYQSbMCab6h6WrhLG5HA1tupKyNzdLXBqir9X+ZpIlkGTBL0inpNSRpz41cw6xeDsy2KTU0a90dwOvAdJJpKP93/QMjYiFJu/BfJf2dj5sS7gZOqHv4B5xPOlubpBl83Dvk/wMHKXml0hHA2xspa2Oz9K0EdlXyOqZDSV77BEngPyMt38vA8UX8TMw24LkyzMwyxjVmM7OMcWA2M8sYB2Yzs4xxYDYzyxgHZjOzjHFgNjPLGAdmM7OM+T8yieBoRWOClAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normalised confusion matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "labelstr=['alive', 'mortality']\n",
    "CM=confusion_matrix(y_test,y_test_pred, )\n",
    "nCM=CM/sum(CM[:])\n",
    "\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=CM,\n",
    "                    display_labels=labelstr)\n",
    "print('confusion matrix')\n",
    "disp.plot();plt.show()\n",
    "\n",
    "ndisp = ConfusionMatrixDisplay(confusion_matrix=nCM,\n",
    "                    display_labels=labelstr)\n",
    "print('normalised confusion matrix')\n",
    "ndisp.plot(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "animated-retail",
   "metadata": {},
   "source": [
    "## Task: Determine the important features for mortality prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "conditional-andorra",
   "metadata": {},
   "source": [
    "### (1) Feature Importance: LASSO-Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "temporal-hierarchy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of features: 59\n",
      "Number of features retained: 18\n",
      "Number of features removed: 41\n",
      "Sparsitiy introduced: 69.49%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_name</th>\n",
       "      <th>coef</th>\n",
       "      <th>abs(coef)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GCS_1</td>\n",
       "      <td>-0.433529</td>\n",
       "      <td>0.433529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GCS_12</td>\n",
       "      <td>-0.386483</td>\n",
       "      <td>0.386483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GCS (total)</td>\n",
       "      <td>0.268892</td>\n",
       "      <td>0.268892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>ICU_Coronary Care Unit</td>\n",
       "      <td>-0.251607</td>\n",
       "      <td>0.251607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Heart Rate</td>\n",
       "      <td>-0.180575</td>\n",
       "      <td>0.180575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Sex</td>\n",
       "      <td>-0.158423</td>\n",
       "      <td>0.158423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>ICU_Cardiac Surgery Recovery Unit</td>\n",
       "      <td>-0.157218</td>\n",
       "      <td>0.157218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Respiratory rate</td>\n",
       "      <td>0.1471</td>\n",
       "      <td>0.1471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Oxygen saturation</td>\n",
       "      <td>-0.114465</td>\n",
       "      <td>0.114465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>GCS_3</td>\n",
       "      <td>-0.112442</td>\n",
       "      <td>0.112442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>pH</td>\n",
       "      <td>-0.111345</td>\n",
       "      <td>0.111345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>GCS_38</td>\n",
       "      <td>0.097256</td>\n",
       "      <td>0.097256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>GCS_36</td>\n",
       "      <td>0.062763</td>\n",
       "      <td>0.062763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GCS_14</td>\n",
       "      <td>0.052386</td>\n",
       "      <td>0.052386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Weight</td>\n",
       "      <td>-0.034831</td>\n",
       "      <td>0.034831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GCS5</td>\n",
       "      <td>0.032016</td>\n",
       "      <td>0.032016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Mean blood pressure</td>\n",
       "      <td>0.00919</td>\n",
       "      <td>0.00919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>GCS_39</td>\n",
       "      <td>-0.000786</td>\n",
       "      <td>0.000786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>GCS_4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>GCS_37</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>GCS_38</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>GCS_38</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>GCS_40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>GCS_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>GCS_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>GCS_8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>GCS_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>GCS_35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Height</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Temperature</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Systolic blood pressure</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>GCS_35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Diastolic blood pressure</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>GCS_34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>GCS_33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GCS 10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GCS_11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GCS_13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GCS_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GCS_16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GCS_17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GCS_18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GCS_19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>GCS_2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>GCS_20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>GCS_21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>GCS_22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>GCS_23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>GCS_24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>GCS_24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>GCS_25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>GCS_26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>GCS_27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>GCS_27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>GCS_28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>GCS_29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>GCS_30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>GCS_32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>GCS_31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         feature_name      coef abs(coef)\n",
       "4                               GCS_1 -0.433529  0.433529\n",
       "6                              GCS_12 -0.386483  0.386483\n",
       "1                         GCS (total)  0.268892  0.268892\n",
       "50             ICU_Coronary Care Unit -0.251607  0.251607\n",
       "47                         Heart Rate -0.180575  0.180575\n",
       "54                                Sex -0.158423  0.158423\n",
       "49  ICU_Cardiac Surgery Recovery Unit -0.157218  0.157218\n",
       "53                   Respiratory rate    0.1471    0.1471\n",
       "52                  Oxygen saturation -0.114465  0.114465\n",
       "27                              GCS_3 -0.112442  0.112442\n",
       "58                                 pH -0.111345  0.111345\n",
       "37                             GCS_38  0.097256  0.097256\n",
       "35                             GCS_36  0.062763  0.062763\n",
       "8                              GCS_14  0.052386  0.052386\n",
       "57                             Weight -0.034831  0.034831\n",
       "3                                GCS5  0.032016  0.032016\n",
       "51                Mean blood pressure   0.00919   0.00919\n",
       "40                             GCS_39 -0.000786  0.000786\n",
       "41                              GCS_4       0.0       0.0\n",
       "36                             GCS_37       0.0       0.0\n",
       "38                             GCS_38       0.0       0.0\n",
       "39                             GCS_38       0.0       0.0\n",
       "42                             GCS_40       0.0       0.0\n",
       "43                              GCS_6       0.0       0.0\n",
       "44                              GCS_7       0.0       0.0\n",
       "45                              GCS_8       0.0       0.0\n",
       "46                              GCS_9       0.0       0.0\n",
       "33                             GCS_35       0.0       0.0\n",
       "48                             Height       0.0       0.0\n",
       "56                        Temperature       0.0       0.0\n",
       "55            Systolic blood pressure       0.0       0.0\n",
       "34                             GCS_35       0.0       0.0\n",
       "0            Diastolic blood pressure       0.0       0.0\n",
       "32                             GCS_34       0.0       0.0\n",
       "31                             GCS_33       0.0       0.0\n",
       "2                              GCS 10       0.0       0.0\n",
       "5                              GCS_11       0.0       0.0\n",
       "7                              GCS_13       0.0       0.0\n",
       "9                              GCS_15       0.0       0.0\n",
       "10                             GCS_16       0.0       0.0\n",
       "11                             GCS_17       0.0       0.0\n",
       "12                             GCS_18       0.0       0.0\n",
       "13                             GCS_19       0.0       0.0\n",
       "14                              GCS_2       0.0       0.0\n",
       "15                             GCS_20       0.0       0.0\n",
       "16                             GCS_21       0.0       0.0\n",
       "17                             GCS_22       0.0       0.0\n",
       "18                             GCS_23       0.0       0.0\n",
       "19                             GCS_24       0.0       0.0\n",
       "20                             GCS_24       0.0       0.0\n",
       "21                             GCS_25       0.0       0.0\n",
       "22                             GCS_26       0.0       0.0\n",
       "23                             GCS_27       0.0       0.0\n",
       "24                             GCS_27       0.0       0.0\n",
       "25                             GCS_28       0.0       0.0\n",
       "26                             GCS_29       0.0       0.0\n",
       "28                             GCS_30       0.0       0.0\n",
       "30                             GCS_32       0.0       0.0\n",
       "29                             GCS_31       0.0       0.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''determine the remaning number of important features'''\n",
    "coef_l1_LR = clf_l1_LR.coef_.ravel()\n",
    "sparsity_l1_LR = np.mean(coef_l1_LR == 0) * 100\n",
    "print('Total number of features: {:}\\nNumber of features retained: {:}\\nNumber of features removed: {:}\\nSparsitiy introduced: {:.2f}%'. format(len(coef_l1_LR), np.sum(coef_l1_LR != 0), np.sum(coef_l1_LR == 0), sparsity_l1_LR))\n",
    "\n",
    "#create a feature ranking table\n",
    "data=np.transpose([feature_names_1, coef_l1_LR, np.absolute(coef_l1_LR)])\n",
    "FEATURE_RANKING=pd.DataFrame(data=data, columns=['feature_name', 'coef', 'abs(coef)'])\n",
    "#order by best -> worst\n",
    "FEATURE_RANKING.sort_values(by=['abs(coef)'],  ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "turned-devon",
   "metadata": {},
   "source": [
    "We can see that the Glasgow Coma Scale (GCS) total score an important predictor of mortality. Other physiological measurements such as the patient's heart rate, temperature, pH or blood pressure are also identifed as predictive of mortality."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "imposed-contents",
   "metadata": {},
   "source": [
    "### (2) Feature Importance: Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "immune-necklace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_importance_sorted_idx = np.argsort(clf_RF.feature_importances_)\n",
    "feature_importance=clf_RF.feature_importances_[tree_importance_sorted_idx]\n",
    "tree_indices = np.arange(0, len(feature_importance)) + 0.5\n",
    "\n",
    "#take the first 25 features\n",
    "nfeats=25\n",
    "tree_importance_sorted_idx=tree_importance_sorted_idx[-nfeats:]\n",
    "feature_importance=feature_importance[-nfeats:]\n",
    "tree_indices = np.arange(0, nfeats) + 0.5\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(12, 8))\n",
    "ax.barh(tree_indices,\n",
    "         feature_importance, height=0.7)\n",
    "ax.set_ylim((0, len(tree_indices)))\n",
    "ax.set_yticks(tree_indices)\n",
    "ax.set_yticklabels(feature_names_1[tree_importance_sorted_idx])\n",
    "ax.set_xlabel('feature importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "seasonal-mattress",
   "metadata": {},
   "source": [
    "Again, we can see that the Glasgow Coma Scale (GCS) total score is the most important predictor of mortality. Other physiological measurements such as the patient's heart rate, temperature, or blood pressure are also identifed as predictive of mortality."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "desirable-advisory",
   "metadata": {},
   "source": [
    "# Task: Re-implement time-series specific features \n",
    "For this example, we will implement use the assumptions of autoregressive models (AR) to evaluate time-series specific features. We can estimate the relationship between the value at any point $t$, $x_t$, and the value at any point $(t-1)$, $x_{t-1}$ using:\n",
    "\n",
    "\\begin{equation}\n",
    "    AR(1)=x_t\\approx a_1x_{t-1}+c\n",
    "\\end{equation}\n",
    "\n",
    "where $a_1$ are the weights of the model and c is the constant. $AR(1)$ denotes that this is a first order model.\n",
    "\n",
    "\n",
    "We can compute the autocorrelation within the time-series $\\mathbf{x}\\in\\mathbb{R}^{1\\times N}$ [9, 10]. The autocorrelation function measures the correlation between $x_t$ and $x_{t+k}$, at various lags, $k$, where $k = 0,\\ldots,K$. The autocorrelation for lag $k$ is: \n",
    "\\begin{equation}a_k=\\frac{c_k}{c_0} \\end{equation} \n",
    "\n",
    "\\begin{equation} c_k=\\frac{1}{T}\\sum\\limits_{t=1}^{T-k}(x_t - \\mathbf{\\bar{x}})(x_{t+k}-\\mathbf{\\bar{x}}) \\end{equation}\n",
    "where $\\mathbf{\\bar{x}}$ is the mean of $\\mathbf{x}$; $c_0$ is the sample variance of the time-series.\n",
    "\n",
    "\n",
    "In this work we can extract the auto-correlation of some physiological measurement at various lags, $k$, in order to extract information about stochasticity. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "american-strength",
   "metadata": {},
   "source": [
    "First pull out the continous feature values, i.e. the physiological measurements such as heart rate and respiratoration. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "quick-singapore",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X data (shape) (9622, 48, 7)\n"
     ]
    }
   ],
   "source": [
    "fidx=np.isin(feature_names, ['Heart Rate', 'Respiratory rate', 'Mean blood pressure', 'Systolic blood pressure', 'Diastolic blood pressure', 'Temperature', 'Oxygen saturation'])\n",
    "X_=X[:, :, fidx]\n",
    "print('X data (shape)', X_.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "static-receipt",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''define our autocorrelation function (ACF)'''\n",
    "#see https://stackoverflow.com/questions/643699/how-can-i-use-numpy-correlate-to-do-autocorrelation \n",
    "def autocorrelation(x,nlags=15):\n",
    "    '''normalised, full autocorrelation'''\n",
    "    lags=np.arange(nlags)\n",
    "    mean=x.mean()\n",
    "    var=np.var(x)\n",
    "    xp=x-mean\n",
    "    corr=np.correlate(xp,xp,'full')[len(x)-1:]/var/len(x)\n",
    "\n",
    "    return corr[:len(lags)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "continuous-replica",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''plot the autocorrelation for varying time lags'''\n",
    "xx=X[example_mort, :, feature_names=='Heart Rate'].squeeze()\n",
    "nlags=np.floor(len(xx)).astype(int)\n",
    "lags=np.arange(nlags)\n",
    "\n",
    "r=autocorrelation(xx, nlags=nlags)\n",
    "plt.stem(r)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "brutal-subsection",
   "metadata": {},
   "source": [
    "We can see the autocorrelation of the heart rate for varying time lags. Below we can use the statsmodels.py plots to generate an ACF with confidence limits. See https://www.statsmodels.org/dev/generated/statsmodels.graphics.tsaplots.plot_acf.html for more details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "liked-verification",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''plot the autocorrelation for varying time lags'''\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8))\n",
    "\n",
    "ax1.plot(xx)\n",
    "\n",
    "ax1.set_ylabel('feature')\n",
    "ax1.set_xlabel('time')\n",
    "\n",
    "# Use the Autocorrelation function\n",
    "# from the statsmodel library passing\n",
    "plot_acf(x=xx,  lags=lags, ax=ax2)\n",
    "\n",
    "ax2.set_ylabel('r')\n",
    "ax2.set_xlabel('lag')\n",
    "ax2.set_xticks(lags)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interim-progressive",
   "metadata": {},
   "source": [
    "Next we can extract autocorrelation features over entire dataset of continuous feature values, i.e. the physiological measurements such as heart rate and respiratoration. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dried-brazilian",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-25-a15149d2ac5b>:9: RuntimeWarning: invalid value encountered in divide\n",
      "  corr=np.correlate(xp,xp,'full')[len(x)-1:]/var/len(x)\n"
     ]
    }
   ],
   "source": [
    "'''extract autocorrelation features over the data'''\n",
    "def extract_autocorr_features(data, nlags):\n",
    "    '''function to extract autocorrelation features on a data matrix'''\n",
    "    rcorr = np.zeros((data.shape[0], nlags, data.shape[2]))\n",
    "    for i, x in enumerate(data):\n",
    "        x=np.transpose(x)\n",
    "        for j, xx in enumerate(x):\n",
    "            rcorr[i, :, j]=autocorrelation(xx, nlags=nlags)       \n",
    "\n",
    "    rcorr[np.isnan(rcorr)]=0        \n",
    "\n",
    "    return rcorr\n",
    "\n",
    "nlags=np.floor(X_.shape[1]/2).astype(int)\n",
    "XAC=extract_autocorr_features(X_, nlags=48)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "retained-wealth",
   "metadata": {},
   "source": [
    "We can then combine these time-series specific features with the rest of the categorical variables.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "every-concentrate",
   "metadata": {},
   "outputs": [],
   "source": [
    "#combine these time-series specific features with the rest of the categorical variables\n",
    "XAC=XAC.reshape(-1, XAC.shape[1]*XAC.shape[2])\n",
    "X_data_new=np.concatenate((X_data_1[:, ~fidx], XAC), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "public-image",
   "metadata": {},
   "source": [
    "## Task: train your models again and compare model performance between statistcial and time-series feature types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dangerous-median",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_data_new, y, test_size=0.33, random_state=42, stratify=y)\n",
    "\n",
    "X_test=zscore_data(X_test)\n",
    "X_train=zscore_data(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "diagnostic-wrong",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logstic Regression\n",
      "Accuracy: 0.735 | Balanced Accuracy: 0.705 | AUROC: 0.772 | Kappa: 0.305 | F1: 0.460\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "clf_l1_LR=LogisticRegression(random_state=42, penalty='l1', C=LAMBDA, solver=\"saga\", tol=0.001, class_weight='balanced')\n",
    "clf_l1_LR.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = clf_l1_LR.predict(X_train)\n",
    "y_test_pred = clf_l1_LR.predict(X_test)\n",
    "y_test_prob = clf_l1_LR.predict_proba(X_test) \n",
    "\n",
    "print('Logstic Regression')\n",
    "print_scores(compute_scores(y_test, y_test_pred, y_test_prob))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "split-exploration",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.5s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    2.0s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    4.6s\n",
      "[Parallel(n_jobs=4)]: Done 792 tasks      | elapsed:    8.2s\n",
      "[Parallel(n_jobs=4)]: Done 1242 tasks      | elapsed:   12.8s\n",
      "[Parallel(n_jobs=4)]: Done 1792 tasks      | elapsed:   18.2s\n",
      "[Parallel(n_jobs=4)]: Done 2000 out of 2000 | elapsed:   20.4s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 792 tasks      | elapsed:    0.3s\n",
      "[Parallel(n_jobs=4)]: Done 1242 tasks      | elapsed:    0.5s\n",
      "[Parallel(n_jobs=4)]: Done 1792 tasks      | elapsed:    0.8s\n",
      "[Parallel(n_jobs=4)]: Done 2000 out of 2000 | elapsed:    0.9s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 792 tasks      | elapsed:    0.3s\n",
      "[Parallel(n_jobs=4)]: Done 1242 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=4)]: Done 1792 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=4)]: Done 2000 out of 2000 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 792 tasks      | elapsed:    0.3s\n",
      "[Parallel(n_jobs=4)]: Done 1242 tasks      | elapsed:    0.4s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "Accuracy: 0.675 | Balanced Accuracy: 0.713 | AUROC: 0.796 | Kappa: 0.271 | F1: 0.448\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done 1792 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=4)]: Done 2000 out of 2000 | elapsed:    0.7s finished\n"
     ]
    }
   ],
   "source": [
    "from imblearn.ensemble import BalancedRandomForestClassifier\n",
    "\n",
    "clf_RF = BalancedRandomForestClassifier(\n",
    "    n_estimators=2000,\n",
    "    replacement=True,\n",
    "    sampling_strategy='not minority',\n",
    "    oob_score=True,\n",
    "    n_jobs=4,\n",
    "    random_state=42,\n",
    "    verbose=1\n",
    ")\n",
    "clf_RF.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = clf_RF.predict(X_train)\n",
    "y_test_pred = clf_RF.predict(X_test)\n",
    "y_test_prob = clf_RF.predict_proba(X_test) \n",
    "\n",
    "print('Random Forest')\n",
    "print_scores(compute_scores(y_test, y_test_pred, y_test_prob))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "restricted-uruguay",
   "metadata": {},
   "source": [
    "We can see we get a modest improvement in performance with the addition of these new features. Whilst the ACF allows us to capture the temporal properties of a signal and it's stochasticity, we are limited by how our features can capture this information. These approaches, however, are constrained transformations and approximations of ambulatory function which are based on prior assumptions. Hand-crafted gait features are often established signal-processing or statistical metrics re-purposed as surrogates to represent timporal aspects of a signal; for instance, extracting the variance in a sensor signal in an attempt to variability. There however may be greater power in allowing an algorithm to learn its own features, termed representation learning. Deep learning is an overarching term given to representation learning, where multiple levels of representation are obtained through the combination of a number of stacked (hence deep) non-linear model layers. Deep learning models typically describe convolutional neural networks (CNN), deep neural networks (DNN), and combined fully-connected deep convolutional neural networks (DCNN) architectures. Other architectures include recurrent neural networks (RNN), such as Long Short Term Memory (LSTM) networks, which are especially adept at modelling sequential time-series data. \n",
    "\n",
    "In the next lectures and lab we will look at how we can use RNNs to model this time-series based mortality prediction task. \n",
    "\n",
    "<em>Aside</em>: Another task would be to look at the spectral properties of the signal and generate new features. You will learn more about data transformations in Wednesday's lecture."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "important-netherlands",
   "metadata": {},
   "source": [
    "### References\n",
    "1. T. Hastie, R. Tibshirani, and J. Friedman,The elements of statistical learning: data mining,inference, and prediction.  Springer Science & Business Media, 2009.\n",
    "1. C. R. Rao, Linear statistical inference and its applications.  Wiley New York, 1973, vol. 2.\n",
    "1. P. McCullagh,Generalized linear models.  Routledge, 2018.\n",
    "1. D. W. Hosmer Jr, S. Lemeshow, and R. X. Sturdivant,Applied logistic regression.  John Wiley &Sons, 2013, vol. 398.\n",
    "1. T. Hastie, R. Tibshirani, and M. Wainwright,Statistical learning with sparsity: the lasso and generalizations.  CRC press, 2015.\n",
    "1. R. Tibshirani, “Regression shrinkage and selection via the lasso,”Journal of the Royal Statistical Society. Series B (Methodological), pp. 267–288, 1996.\n",
    "1. L. Breiman, “Random forests,”Machine learning, vol. 45, no. 1, pp. 5–32, 2001.\n",
    "1. C. Strobl, A.-L. Boulesteix, T. Kneib, T. Augustin, and A. Zeileis, “Conditional variable importancefor random forests,”BMC bioinformatics, vol. 9, no. 1, p. 307, 2008.\n",
    "1. Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons\n",
    "1. Barber, D. (2012). Bayesian reasoning and machine learning. Cambridge University Press."
   ]
  }
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